{"id":58209,"date":"2025-12-30T19:30:50","date_gmt":"2025-12-30T19:30:50","guid":{"rendered":"https:\/\/www.devopsschool.com\/blog\/?p=58209"},"modified":"2026-01-18T19:34:12","modified_gmt":"2026-01-18T19:34:12","slug":"top-10-human-in-the-loop-labeling-tools-features-pros-cons-comparison","status":"publish","type":"post","link":"https:\/\/www.devopsschool.com\/blog\/top-10-human-in-the-loop-labeling-tools-features-pros-cons-comparison\/","title":{"rendered":"Top 10 Human-in-the-Loop Labeling Tools: Features, Pros, Cons & Comparison"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

Introduction<\/strong><\/h2>\n\n\n\n

Human-in-the-Loop (HITL) labeling tools are platforms that combine machine assistance with human judgment<\/strong> to create high-quality labeled datasets for artificial intelligence and machine learning models. Instead of relying purely on automation or fully manual annotation, these tools introduce humans at critical decision points\u2014reviewing, correcting, validating, and improving model outputs.<\/p>\n\n\n\n

HITL labeling has become essential as AI systems move into high-stakes, real-world environments<\/strong> such as healthcare, finance, autonomous systems, and enterprise analytics. In these domains, errors in training data can lead to biased, unsafe, or unreliable models. Human oversight ensures accuracy, context awareness, and continuous learning loops.<\/p>\n\n\n\n

Common real-world use cases include:<\/strong><\/p>\n\n\n\n

    \n
  • Computer vision annotation for autonomous driving and surveillance<\/li>\n\n\n\n
  • NLP labeling for chatbots, search, and document intelligence<\/li>\n\n\n\n
  • Speech and audio transcription<\/li>\n\n\n\n
  • Model retraining, error correction, and active learning workflows<\/li>\n<\/ul>\n\n\n\n

    When choosing a Human-in-the-Loop labeling tool, users should evaluate:<\/p>\n\n\n\n

      \n
    • Supported data types (text, image, video, audio, multimodal)<\/li>\n\n\n\n
    • Workflow automation and quality control mechanisms<\/li>\n\n\n\n
    • Scalability and collaboration features<\/li>\n\n\n\n
    • Security, compliance, and deployment flexibility<\/li>\n\n\n\n
    • Integration with ML pipelines and MLOps tools<\/li>\n<\/ul>\n\n\n\n

      Best for:<\/strong>
      Human-in-the-Loop labeling tools are ideal for ML engineers, data scientists, AI product teams, enterprises, research labs, and regulated-industry organizations<\/strong> that require high-quality, auditable, and continuously improving datasets.<\/p>\n\n\n\n

      Not ideal for:<\/strong>
      They may be unnecessary for small experiments, synthetic-data-only projects, or low-risk prototypes<\/strong> where automated labeling or weak supervision is sufficient.<\/p>\n\n\n\n

      \n\n\n\n

      Top 10 Human-in-the-Loop Labeling Tools<\/strong><\/h2>\n\n\n\n
      \n\n\n\n

      1 \u2014 Labelbox<\/strong><\/h3>\n\n\n\n

      Short description:<\/strong>
      An enterprise-grade data labeling and training data platform designed for computer vision, NLP, and multimodal AI workflows.<\/p>\n\n\n\n

      Key features:<\/strong><\/p>\n\n\n\n

        \n
      • Image, video, text, and geospatial annotation<\/li>\n\n\n\n
      • Active learning and model-assisted labeling<\/li>\n\n\n\n
      • Custom workflows with review and consensus stages<\/li>\n\n\n\n
      • Quality metrics and inter-annotator agreement<\/li>\n\n\n\n
      • Workforce management and role-based access<\/li>\n\n\n\n
      • Dataset versioning and audit trails<\/li>\n<\/ul>\n\n\n\n

        Pros:<\/strong><\/p>\n\n\n\n

          \n
        • Strong enterprise-level workflow customization<\/li>\n\n\n\n
        • Excellent support for computer vision use cases<\/li>\n\n\n\n
        • Scales well with large labeling teams<\/li>\n<\/ul>\n\n\n\n

          Cons:<\/strong><\/p>\n\n\n\n

            \n
          • Premium pricing for advanced features<\/li>\n\n\n\n
          • Learning curve for complex workflows<\/li>\n<\/ul>\n\n\n\n

            Security & compliance:<\/strong>
            SSO, encryption at rest and in transit, audit logs, SOC 2, GDPR support<\/p>\n\n\n\n

            Support & community:<\/strong>
            Comprehensive documentation, enterprise onboarding, dedicated support<\/p>\n\n\n\n

            \n\n\n\n

            2 \u2014 Scale AI<\/strong><\/h3>\n\n\n\n

            Short description:<\/strong>
            A managed data engine combining automation and expert human reviewers for high-accuracy AI training data.<\/p>\n\n\n\n

            Key features:<\/strong><\/p>\n\n\n\n

              \n
            • Human-verified labeling at scale<\/li>\n\n\n\n
            • Active learning pipelines<\/li>\n\n\n\n
            • Support for text, image, video, 3D, and sensor data<\/li>\n\n\n\n
            • Automated quality assurance<\/li>\n\n\n\n
            • Custom task orchestration<\/li>\n\n\n\n
            • High-volume workforce management<\/li>\n<\/ul>\n\n\n\n

              Pros:<\/strong><\/p>\n\n\n\n

                \n
              • Extremely high annotation accuracy<\/li>\n\n\n\n
              • Handles complex and safety-critical datasets<\/li>\n<\/ul>\n\n\n\n

                Cons:<\/strong><\/p>\n\n\n\n

                  \n
                • Less self-service compared to tooling platforms<\/li>\n\n\n\n
                • Expensive for small teams<\/li>\n<\/ul>\n\n\n\n

                  Security & compliance:<\/strong>
                  SOC 2, GDPR, ISO-aligned controls, secure data handling<\/p>\n\n\n\n

                  Support & community:<\/strong>
                  White-glove enterprise support, limited open community<\/p>\n\n\n\n

                  \n\n\n\n

                  3 \u2014 SuperAnnotate<\/strong><\/h3>\n\n\n\n

                  Short description:<\/strong>
                  A collaborative annotation platform focused on fast, accurate computer vision labeling with human review loops.<\/p>\n\n\n\n

                  Key features:<\/strong><\/p>\n\n\n\n

                    \n
                  • Image and video annotation<\/li>\n\n\n\n
                  • Annotation automation with human correction<\/li>\n\n\n\n
                  • Quality control dashboards<\/li>\n\n\n\n
                  • Collaboration and task assignment<\/li>\n\n\n\n
                  • Dataset versioning<\/li>\n\n\n\n
                  • API and SDK access<\/li>\n<\/ul>\n\n\n\n

                    Pros:<\/strong><\/p>\n\n\n\n

                      \n
                    • User-friendly interface<\/li>\n\n\n\n
                    • Strong productivity tools for CV teams<\/li>\n<\/ul>\n\n\n\n

                      Cons:<\/strong><\/p>\n\n\n\n

                        \n
                      • Limited NLP compared to vision focus<\/li>\n\n\n\n
                      • Advanced automation requires configuration<\/li>\n<\/ul>\n\n\n\n

                        Security & compliance:<\/strong>
                        SSO, encryption, GDPR support<\/p>\n\n\n\n

                        Support & community:<\/strong>
                        Good documentation, responsive customer support<\/p>\n\n\n\n

                        \n\n\n\n

                        4 \u2014 Label Studio<\/strong><\/h3>\n\n\n\n

                        Short description:<\/strong>
                        An open-source data labeling platform offering flexible, human-in-the-loop annotation for multiple data types.<\/p>\n\n\n\n

                        Key features:<\/strong><\/p>\n\n\n\n

                          \n
                        • Supports text, image, audio, video, time-series<\/li>\n\n\n\n
                        • Fully customizable labeling interfaces<\/li>\n\n\n\n
                        • ML-assisted pre-labeling<\/li>\n\n\n\n
                        • Review and approval workflows<\/li>\n\n\n\n
                        • Self-hosted or managed options<\/li>\n\n\n\n
                        • Plugin ecosystem<\/li>\n<\/ul>\n\n\n\n

                          Pros:<\/strong><\/p>\n\n\n\n

                            \n
                          • Highly flexible and extensible<\/li>\n\n\n\n
                          • Strong open-source community<\/li>\n<\/ul>\n\n\n\n

                            Cons:<\/strong><\/p>\n\n\n\n

                              \n
                            • Enterprise features require paid edition<\/li>\n\n\n\n
                            • Requires setup and maintenance<\/li>\n<\/ul>\n\n\n\n

                              Security & compliance:<\/strong>
                              Varies by deployment; self-hosted controls available<\/p>\n\n\n\n

                              Support & community:<\/strong>
                              Active open-source community, commercial support available<\/p>\n\n\n\n

                              \n\n\n\n

                              5 \u2014 Amazon SageMaker Ground Truth<\/strong><\/h3>\n\n\n\n

                              Short description:<\/strong>
                              A managed HITL labeling service integrated into the AWS machine learning ecosystem.<\/p>\n\n\n\n

                              Key features:<\/strong><\/p>\n\n\n\n

                                \n
                              • Built-in human review workflows<\/li>\n\n\n\n
                              • Active learning and automated labeling<\/li>\n\n\n\n
                              • Integration with SageMaker pipelines<\/li>\n\n\n\n
                              • Private and vendor workforce options<\/li>\n\n\n\n
                              • Quality sampling and audit features<\/li>\n\n\n\n
                              • Scalable cloud infrastructure<\/li>\n<\/ul>\n\n\n\n

                                Pros:<\/strong><\/p>\n\n\n\n

                                  \n
                                • Seamless AWS ecosystem integration<\/li>\n\n\n\n
                                • Scales automatically with workloads<\/li>\n<\/ul>\n\n\n\n

                                  Cons:<\/strong><\/p>\n\n\n\n

                                    \n
                                  • AWS lock-in<\/li>\n\n\n\n
                                  • UI flexibility is limited<\/li>\n<\/ul>\n\n\n\n

                                    Security & compliance:<\/strong>
                                    AWS security model, IAM, encryption, compliance certifications<\/p>\n\n\n\n

                                    Support & community:<\/strong>
                                    AWS documentation, enterprise support plans<\/p>\n\n\n\n

                                    \n\n\n\n

                                    6 \u2014 Prodigy<\/strong><\/h3>\n\n\n\n

                                    Short description:<\/strong>
                                    A developer-focused annotation tool optimized for NLP workflows and active learning loops.<\/p>\n\n\n\n

                                    Key features:<\/strong><\/p>\n\n\n\n

                                      \n
                                    • Scriptable annotation workflows<\/li>\n\n\n\n
                                    • Active learning and model feedback loops<\/li>\n\n\n\n
                                    • Tight integration with NLP pipelines<\/li>\n\n\n\n
                                    • Real-time human correction<\/li>\n\n\n\n
                                    • Local deployment support<\/li>\n\n\n\n
                                    • Custom labeling recipes<\/li>\n<\/ul>\n\n\n\n

                                      Pros:<\/strong><\/p>\n\n\n\n

                                        \n
                                      • Extremely fast for NLP iteration<\/li>\n\n\n\n
                                      • Highly customizable for engineers<\/li>\n<\/ul>\n\n\n\n

                                        Cons:<\/strong><\/p>\n\n\n\n

                                          \n
                                        • Minimal UI for non-technical users<\/li>\n\n\n\n
                                        • Limited enterprise governance features<\/li>\n<\/ul>\n\n\n\n

                                          Security & compliance:<\/strong>
                                          Varies by deployment; local-first approach<\/p>\n\n\n\n

                                          Support & community:<\/strong>
                                          Strong documentation, developer-centric community<\/p>\n\n\n\n

                                          \n\n\n\n

                                          7 \u2014 Dataloop<\/strong><\/h3>\n\n\n\n

                                          Short description:<\/strong>
                                          An end-to-end data management and labeling platform with built-in human-in-the-loop AI operations.<\/p>\n\n\n\n

                                          Key features:<\/strong><\/p>\n\n\n\n

                                            \n
                                          • Multimodal data labeling<\/li>\n\n\n\n
                                          • Automated pipelines with human checkpoints<\/li>\n\n\n\n
                                          • Dataset lifecycle management<\/li>\n\n\n\n
                                          • Model monitoring and feedback<\/li>\n\n\n\n
                                          • API-driven workflows<\/li>\n\n\n\n
                                          • Collaboration and governance tools<\/li>\n<\/ul>\n\n\n\n

                                            Pros:<\/strong><\/p>\n\n\n\n

                                              \n
                                            • Strong MLOps integration<\/li>\n\n\n\n
                                            • Unified data and labeling lifecycle<\/li>\n<\/ul>\n\n\n\n

                                              Cons:<\/strong><\/p>\n\n\n\n

                                                \n
                                              • Feature-rich UI can feel complex<\/li>\n\n\n\n
                                              • Best suited for mature teams<\/li>\n<\/ul>\n\n\n\n

                                                Security & compliance:<\/strong>
                                                SSO, encryption, GDPR-aligned controls<\/p>\n\n\n\n

                                                Support & community:<\/strong>
                                                Enterprise support, structured onboarding<\/p>\n\n\n\n

                                                \n\n\n\n

                                                8 \u2014 Snorkel AI<\/strong><\/h3>\n\n\n\n

                                                Short description:<\/strong>
                                                A data-centric AI platform combining programmatic labeling with targeted human validation.<\/p>\n\n\n\n

                                                Key features:<\/strong><\/p>\n\n\n\n

                                                  \n
                                                • Weak supervision and labeling functions<\/li>\n\n\n\n
                                                • Human review of edge cases<\/li>\n\n\n\n
                                                • Data quality and coverage metrics<\/li>\n\n\n\n
                                                • Enterprise governance<\/li>\n\n\n\n
                                                • Active learning loops<\/li>\n\n\n\n
                                                • Integration with ML pipelines<\/li>\n<\/ul>\n\n\n\n

                                                  Pros:<\/strong><\/p>\n\n\n\n

                                                    \n
                                                  • Reduces labeling cost dramatically<\/li>\n\n\n\n
                                                  • Excellent for large text datasets<\/li>\n<\/ul>\n\n\n\n

                                                    Cons:<\/strong><\/p>\n\n\n\n

                                                      \n
                                                    • Steeper learning curve<\/li>\n\n\n\n
                                                    • Less suited for pure manual labeling<\/li>\n<\/ul>\n\n\n\n

                                                      Security & compliance:<\/strong>
                                                      Enterprise-grade security, audit logs, compliance support<\/p>\n\n\n\n

                                                      Support & community:<\/strong>
                                                      Enterprise training, expert consulting available<\/p>\n\n\n\n

                                                      \n\n\n\n

                                                      9 \u2014 Kili Technology<\/strong><\/h3>\n\n\n\n

                                                      Short description:<\/strong>
                                                      A modern data labeling tool emphasizing human quality control and annotation productivity.<\/p>\n\n\n\n

                                                      Key features:<\/strong><\/p>\n\n\n\n

                                                        \n
                                                      • Image, video, and text annotation<\/li>\n\n\n\n
                                                      • Quality assurance workflows<\/li>\n\n\n\n
                                                      • Reviewer consensus scoring<\/li>\n\n\n\n
                                                      • Active learning support<\/li>\n\n\n\n
                                                      • API and automation<\/li>\n\n\n\n
                                                      • Team collaboration features<\/li>\n<\/ul>\n\n\n\n

                                                        Pros:<\/strong><\/p>\n\n\n\n

                                                          \n
                                                        • Strong focus on annotation accuracy<\/li>\n\n\n\n
                                                        • Clean and intuitive interface<\/li>\n<\/ul>\n\n\n\n

                                                          Cons:<\/strong><\/p>\n\n\n\n

                                                            \n
                                                          • Smaller ecosystem than larger vendors<\/li>\n\n\n\n
                                                          • Advanced automation still evolving<\/li>\n<\/ul>\n\n\n\n

                                                            Security & compliance:<\/strong>
                                                            GDPR support, encryption, access controls<\/p>\n\n\n\n

                                                            Support & community:<\/strong>
                                                            Good documentation, responsive customer success<\/p>\n\n\n\n

                                                            \n\n\n\n

                                                            10 \u2014 V7<\/strong><\/h3>\n\n\n\n

                                                            Short description:<\/strong>
                                                            A computer-vision-focused annotation platform combining automation with human review.<\/p>\n\n\n\n

                                                            Key features:<\/strong><\/p>\n\n\n\n

                                                              \n
                                                            • AI-assisted image and video labeling<\/li>\n\n\n\n
                                                            • Human validation loops<\/li>\n\n\n\n
                                                            • Dataset management<\/li>\n\n\n\n
                                                            • Model performance tracking<\/li>\n\n\n\n
                                                            • Cloud-native architecture<\/li>\n\n\n\n
                                                            • Collaboration tools<\/li>\n<\/ul>\n\n\n\n

                                                              Pros:<\/strong><\/p>\n\n\n\n

                                                                \n
                                                              • Fast annotation workflows<\/li>\n\n\n\n
                                                              • Strong automation for CV teams<\/li>\n<\/ul>\n\n\n\n

                                                                Cons:<\/strong><\/p>\n\n\n\n

                                                                  \n
                                                                • Limited NLP capabilities<\/li>\n\n\n\n
                                                                • Best for vision-centric projects<\/li>\n<\/ul>\n\n\n\n

                                                                  Security & compliance:<\/strong>
                                                                  Encryption, access controls, GDPR support<\/p>\n\n\n\n

                                                                  Support & community:<\/strong>
                                                                  Commercial support, improving documentation<\/p>\n\n\n\n

                                                                  \n\n\n\n

                                                                  Comparison Table<\/strong><\/h2>\n\n\n\n
                                                                  Tool Name<\/th>Best For<\/th>Platform(s) Supported<\/th>Standout Feature<\/th>Rating<\/th><\/tr><\/thead>
                                                                  Labelbox<\/td>Enterprise ML teams<\/td>Cloud, API<\/td>Workflow customization<\/td>N\/A<\/td><\/tr>
                                                                  Scale AI<\/td>High-accuracy datasets<\/td>Managed service<\/td>Human precision at scale<\/td>N\/A<\/td><\/tr>
                                                                  SuperAnnotate<\/td>Computer vision teams<\/td>Web, API<\/td>CV productivity tools<\/td>N\/A<\/td><\/tr>
                                                                  Label Studio<\/td>Flexible labeling needs<\/td>Self-hosted, Cloud<\/td>Open-source extensibility<\/td>N\/A<\/td><\/tr>
                                                                  SageMaker Ground Truth<\/td>AWS users<\/td>Cloud<\/td>AWS-native HITL<\/td>N\/A<\/td><\/tr>
                                                                  Prodigy<\/td>NLP developers<\/td>Local, API<\/td>Active learning loops<\/td>N\/A<\/td><\/tr>
                                                                  Dataloop<\/td>End-to-end AI ops<\/td>Cloud, API<\/td>Data lifecycle management<\/td>N\/A<\/td><\/tr>
                                                                  Snorkel AI<\/td>Large text datasets<\/td>Enterprise<\/td>Weak supervision<\/td>N\/A<\/td><\/tr>
                                                                  Kili Technology<\/td>Quality-focused labeling<\/td>Cloud, API<\/td>Reviewer consensus<\/td>N\/A<\/td><\/tr>
                                                                  V7<\/td>Vision-first AI teams<\/td>Cloud<\/td>Automated CV labeling<\/td>N\/A<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                                                  \n\n\n\n

                                                                  Evaluation & Scoring of Human-in-the-Loop Labeling Tools<\/strong><\/h2>\n\n\n\n
                                                                  Tool<\/th>Core Features (25%)<\/th>Ease of Use (15%)<\/th>Integrations (15%)<\/th>Security (10%)<\/th>Performance (10%)<\/th>Support (10%)<\/th>Value (15%)<\/th>Total Score<\/th><\/tr><\/thead>
                                                                  Labelbox<\/td>9<\/td>8<\/td>8<\/td>9<\/td>9<\/td>9<\/td>7<\/td>8.5<\/strong><\/td><\/tr>
                                                                  Scale AI<\/td>9<\/td>7<\/td>7<\/td>9<\/td>9<\/td>9<\/td>6<\/td>8.3<\/strong><\/td><\/tr>
                                                                  Label Studio<\/td>8<\/td>7<\/td>8<\/td>7<\/td>8<\/td>8<\/td>9<\/td>8.1<\/strong><\/td><\/tr>
                                                                  Prodigy<\/td>7<\/td>6<\/td>7<\/td>6<\/td>8<\/td>7<\/td>8<\/td>7.2<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                                                  \n\n\n\n

                                                                  Which Human-in-the-Loop Labeling Tool Is Right for You?<\/strong><\/h2>\n\n\n\n
                                                                    \n
                                                                  • Solo users \/ researchers:<\/strong> Prodigy, Label Studio<\/li>\n\n\n\n
                                                                  • SMBs:<\/strong> Kili Technology, SuperAnnotate<\/li>\n\n\n\n
                                                                  • Mid-market teams:<\/strong> Labelbox, Dataloop<\/li>\n\n\n\n
                                                                  • Enterprise & regulated industries:<\/strong> Scale AI, Snorkel AI, SageMaker Ground Truth<\/li>\n<\/ul>\n\n\n\n

                                                                    Budget-conscious:<\/strong> Open-source or developer-centric tools
                                                                    Premium solutions:<\/strong> Managed services and enterprise platforms
                                                                    Feature depth vs ease of use:<\/strong> Advanced workflows increase power but add complexity
                                                                    Security & compliance:<\/strong> Essential for healthcare, finance, and government workloads<\/p>\n\n\n\n

                                                                    \n\n\n\n

                                                                    Frequently Asked Questions (FAQs)<\/strong><\/h2>\n\n\n\n

                                                                    1. What does \u201chuman-in-the-loop\u201d mean in labeling?<\/strong>
                                                                    It means humans review, correct, or validate AI-generated labels to improve accuracy.<\/p>\n\n\n\n

                                                                    2. Are HITL tools better than fully automated labeling?<\/strong>
                                                                    Yes, especially for complex, ambiguous, or high-risk datasets.<\/p>\n\n\n\n

                                                                    3. Do these tools support active learning?<\/strong>
                                                                    Most modern platforms integrate active learning to prioritize hard examples.<\/p>\n\n\n\n

                                                                    4. Can HITL tools reduce labeling cost?<\/strong>
                                                                    Yes, by combining automation with selective human review.<\/p>\n\n\n\n

                                                                    5. Are they suitable for regulated industries?<\/strong>
                                                                    Many tools support compliance, audit logs, and access controls.<\/p>\n\n\n\n

                                                                    6. Do I need a large team to use them?<\/strong>
                                                                    No, some tools are designed for individual developers and small teams.<\/p>\n\n\n\n

                                                                    7. Can they integrate with MLOps pipelines?<\/strong>
                                                                    Most enterprise tools offer APIs and pipeline integrations.<\/p>\n\n\n\n

                                                                    8. What data types are supported?<\/strong>
                                                                    Commonly text, image, video, audio, and multimodal data.<\/p>\n\n\n\n

                                                                    9. Are open-source tools production-ready?<\/strong>
                                                                    Yes, with proper deployment and governance.<\/p>\n\n\n\n

                                                                    10. What is the biggest mistake when choosing a tool?<\/strong>
                                                                    Over-buying complexity instead of matching the tool to actual workflow needs.<\/p>\n\n\n\n

                                                                    \n\n\n\n

                                                                    Conclusion<\/strong><\/h2>\n\n\n\n

                                                                    Human-in-the-Loop labeling tools play a critical role in building reliable, ethical, and high-performance AI systems<\/strong>. They bridge the gap between automation and human expertise, ensuring data quality where it matters most.<\/p>\n\n\n\n

                                                                    When selecting a tool, focus on data type support, workflow control, scalability, security, and long-term integration<\/strong> with your AI stack. There is no universal winner\u2014the best solution depends on your team size, industry, risk tolerance, and AI maturity level<\/strong>.<\/p>\n\n\n\n

                                                                    Choosing wisely at the data labeling stage can determine the success\u2014or failure\u2014of everything that follows.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                                                    Introduction Human-in-the-Loop (HITL) labeling tools are platforms that combine machine assistance with human judgment to create high-quality labeled datasets for artificial intelligence and machine learning models. Instead of relying purely… <\/p>\n","protected":false},"author":58,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_joinchat":[],"footnotes":""},"categories":[11138],"tags":[23435,23429,23436,23437,23441,23440,23433,23431,23430,23438,23432,23442,23439,23434],"class_list":["post-58209","post","type-post","status-publish","format-standard","hentry","category-best-tools","tag-active-learning-labeling-systems","tag-ai-data-annotation-platforms","tag-ai-model-data-preparation","tag-ai-quality-assurance-workflows","tag-annotation-review-and-validation-tools","tag-enterprise-data-labeling-solutions","tag-hitl-annotation-software","tag-human-assisted-ai-training","tag-human-in-the-loop-labeling-tools","tag-image-text-video-annotation-tools","tag-machine-learning-data-labeling","tag-mlops-data-labeling-platforms","tag-supervised-learning-data-labeling","tag-training-data-management-tools"],"_links":{"self":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/58209","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/users\/58"}],"replies":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/comments?post=58209"}],"version-history":[{"count":1,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/58209\/revisions"}],"predecessor-version":[{"id":58211,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/58209\/revisions\/58211"}],"wp:attachment":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/media?parent=58209"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/categories?post=58209"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/tags?post=58209"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}